Biped Locomotion
Survey of Intelligent Control Techniques for Humanoid Robots
Journal of Intelligent and Robotic Systems
Fuzzy neural network approaches for robotic gait synthesis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
IEEE Transactions on Neural Networks
A review of gait optimization based on evolutionary computation
Applied Computational Intelligence and Soft Computing - Special issue on theory and applications of evolutionary computation
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A humanoid walking gait synthesizing approach, which is able to generate gaits in both sagittal and frontal planes, is presented in this paper. To further improve the humanoid walking gait in consideration of both ZMP (Zero Moment Point) and energy consumption constraints, a two-stage optimization method is proposed. At the first stage, real-coded GAs (genetic algorithms) are used to generate a set of near-optimal walking gaits. At the second stage, the near-optimal walking gaits are used as training samples for a GA-based NN (neural network) to further improve the humanoid walking gait. By making use of the global optimization capability of GAs, the GA-based NN can solve the local minima problem. The proposed approach is able to generate near-optimal walking gait at any speed in feasible range. Experiments are conducted to verify the effectiveness of the proposed method.